Determination of rutting distresses on hot mix asphalts by advanced techniques
Bibliographic record
Abstract
The sustainable use of non-renewable natural resources, such as asphalt binder, can be achieved by adequate planning. The proper assessment of asphalt binders is a prerequisite to the appropriate designing of road constructions that can eventually result in pavements in which the development of pavement distresses can be mitigated. Rutting is the most common distress occurring at high temperatures, which is frequently experienced by such countries as Indonesia; thus, the use of adequate asphalt binder in hot mix asphalt pavements results in long-lasting road constructions. By means of advanced techniques, e.g., multiple stress creep recovery test, conducted on a dynamic shear rheometer, it is possible to determine the rutting potential of asphalt binders. This technique, however, still seems to be imprecise at currently determined shear stress values. This paper aims to investigate on the example of ten different asphalt binders, if creep and recovery measured at higher shear stresses result in better correlation with rutting potential of hot mix asphalts than that at the standardized stress levels. Concurrently, other conventional asphalt binder properties (e.g., penetration, softening point, elastic recovery) are determined and compared with rutting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".